Pseudomonas syringae infectivity correlates to altered transcript and metabolite levels of Arabidopsis mediator mutants

Rapid metabolic responses to pathogens are essential for plant survival and depend on numerous transcription factors. Mediator is the major transcriptional co-regulator for integration and transmission of signals from transcriptional regulators to RNA polymerase II. Using four Arabidopsis Mediator mutants, med16, med18, med25 and cdk8, we studied how differences in regulation of their transcript and metabolite levels correlate to their responses to Pseudomonas syringae infection. We found that med16 and cdk8 were susceptible, while med25 showed increased resistance. Glucosinolate, phytoalexin and carbohydrate levels were reduced already before infection in med16 and cdk8, but increased in med25, which also displayed increased benzenoids levels. Early after infection, wild type plants showed reduced glucosinolate and nucleoside levels, but increases in amino acids, benzenoids, oxylipins and the phytoalexin camalexin. The Mediator mutants showed altered levels of these metabolites and in regulation of genes encoding key enzymes for their metabolism. At later stage, mutants displayed defective levels of specific amino acids, carbohydrates, lipids and jasmonates which correlated to their infection response phenotypes. Our results reveal that MED16, MED25 and CDK8 are required for a proper, coordinated transcriptional response of genes which encode enzymes involved in important metabolic pathways for Arabidopsis responses to Pseudomonas syringae infections.

Med16, med18, med25, and cdk8 mutants, representing each of the tail, middle, head, and kinase Mediator modules, display diverse defects in defense signaling in response to different infections 6,7,9,20,21 .To investigate their responses to P. syringae, we infected Col-0 and mutants with P. syringae pv.tomato DC3000, a virulent strain that suppresses PTI by injecting effectors into cells via bacterial secretion systems.This generates a mild disease response that becomes visible after 2-3 days in the form of wet, chlorotic, and spreading necrotic lesions 22 .Mediator mutants also display flowering-time phenotypes [23][24][25] .To avoid developmental effects, we infected our plants under non-inductive, short-day conditions at the age of 5-6 weeks.Colony-forming units (CFUs) for each line was determined 72 h post infection (p.i.) using serial dilution (Fig. 1A, B). med16 and cdk8 showed significantly higher, and med25 lower CFUs relative to Col-0.A statistically non-significant increase was also detected in med18 in accordance with other reports 26 .Susceptibility of med16 and cdk8 was also detected as more severe disease symptoms of leaves at 72 h (Fig. 1C).

Global metabolite levels in Col-0 and mutants deviate between infected and mock-treated leaves with the highest differences detected 72 h p.i.
Untargeted LC-MS and GC-MS were performed at 24 and 72 h p.i. to determine global metabolite levels in infected and mock-treated Col-0 and mutants.We recorded 171 and 75 metabolites from the LC-MS and GC-MS, respectively (Supplementary Table 1).For twenty-four metabolites identified in both assays, we decided to use the LC-MS data resulting 222 metabolites used for further analyses.An overview of metabolite levels in Col-0 and mutants at both timepoints after infection or mock-treatment (Control) is illustrated as a principal component analysis (PCA; Fig. 2A).PC1 and PC2 accounted for 42.08% and 11.41% of the total variation, respectively.The profiles in mutants differed slightly from Col-0 under control conditions (PC2) while major differences were detected after infection, especially at 72 h (PC1).The four independent infection replicates for each mutant and treatment grouped well, indicating high reproducibility.
In agreement with their increased susceptibility, med16 and cdk8 showed the largest number of changed metabolite levels at both 24 and 72 h after infection (Fig. 2B).Lists of normalized peak values of all metabolites and their ratios in infected and mock-treated Col-0 and mutants at each time point are shown (Supplementary Tables 1-2).Differences were further visualized using heatmaps and hierarchical clustering (Supplementary Fig. 1) and confirm that the sensitive med16 and cdk8 mutants display pronounced increases of secondary metabolites at 72 h, in particular sugars, lipids, and amino acids.

Uninfected Col-0 and mutants display differences in metabolite levels of specific pathways
To identify metabolites that differ between mock-treated Col-0 and mutants, those that displayed a significant difference at both time points were identified.med16 showed the highest number of decreased and med18 the highest number of increased metabolites (Fig. 3A).To illustrate these differences comprehensively, we grouped them into nineteen categories according to their classification in the Human Metabolome Database (https:// hmdb.ca/; Supplementary Table 3).The mutants displayed significant differences in levels of metabolite categories relative to Col-0 even in mock-treated lines (Fig. 3B).Phytoalexins, carbohydrates and GLSs were reduced in the susceptible med16 and cdk8 but increased in med18 and/or the resistant med25.In support, RNA-seq showed that med16 and cdk8 displayed a pronounced reduction of transcripts encoding GLS biosynthesis enzymes (Fig. 3C,  4).We also noticed reduction in med16 and cdk8 of transcripts belonging GO categories "Glycine, serine and threonine metabolism", "Sulfur metabolism" and "2-oxocarboxylic acid metabolism", which represent pathways linked to GLS metabolism.Finally, med16, med18 and cdk8 showed defects in expression of "plant-pathogen interactions" genes, albeit in different directions where med18 and cdk8 showed decreased, while med16 showed increased levels.Since med16 and cdk8 are susceptible, while med25 shows increased resistance to P. syringae, we focused on metabolites that displayed similar differences in med16 and cdk8 relative to Col-0 in mock-treated plants, and those that where uniquely different from Col-0 in each of med16, med25 and cdk8 at both timepoints.No metabolites were commonly increased in med16 and cdk8 relative to Col-0, but two GLSs (Sulforaphane and a Sulforaphane fragment) and one phytoalexin (Benzyl dithiocarbamate) were decreased and might contribute to susceptibility (Supplementary Table 5).To obtain a broader view of GLS levels in mutants, we calculated their mean in mock-treated mutants relative to Col-0 at the 24-and 72-h time points.Five GLSs (Sulforaphane, a Sulforaphane fragment, Hirsutin, a Hirsutin fragment and Neoglucobrassicin) displayed reduced levels in the sensitive med16 and cdk8 (Supplementary Fig. 2A).In contrast, GLS levels were increased in med18 and med25.We also detected several phytoalexins that were reduced in med16 and cdk8 but increased in med18 and med25 (Supplementary Fig. 2B).
Fourteen metabolites were uniquely decreased in mock-treated med16 and eight in cdk8 at both time points (Supplementary Table 5).They represent various metabolite categories, but five (med16) and three (cdk8) were GLSs or phytoalexins.Using mean values at 24-and 72-h, we identified five (med16) and six (cdk8) downregulated GLSs, and four (med16) and three (cdk8) downregulated phytoalexins (Supplementary Fig. 2A, B).In line with the opposite phenotype of med25 versus med16 and cdk8, GLSs and phytoalexins were upregulated in med25.None of the uniquely decreased metabolites in med25, and none of the uniquely increased metabolites in med16 and cdk8 were GLSs or phytoalexins (Supplementary Table 5).Thus, GLS and phytoalexin levels in mock-treated mutants correlate with their P. syringae susceptibility.Finally, ten metabolites were uniquely increased in med25.The most common were benzenoids (Supplementary Fig. 2C) which are derivatives of SA.

Reduction of metabolite levels during the early infection response is impaired in med16 and med18
Hierarchical clustering of all metabolites displaying changed levels in Col-0 at 24 h relative to its control revealed a set of decreased GLSs and nucleosides, whereas phytoprostanes, lipids, amino acids, benzenoids, jasmonates, and phytoalexins were increased (Fig. 4A, Supplementary Tables 6-7).Mock-treated plants were used as controls rather than untreated to avoid unwanted wounding responses elicited by mechanical manipulation.The reduction of GLSs in Col-0 was absent in med16 and impaired in med18.Similarly, the reduction in nucleosides at the early timepoint in Col-0, was absent in med16 and defect in the other mutants (Supplementary Table 6).Lack of metabolite reduction in mutants can result from different mechanisms.One possibility is that levels are reduced even in mock-treated mutants, and remain low after infection.Alternatively, mutants can have normal levels before infection but are unable to reduce them in response to infection.To distinguish between these possibilities,   we compared the absolute levels for one representative of each GLS class: 5-methylsulfinylpentyl-GLS (aliphatic) and 3-Indolylmethylglucosinolate/Glucobrassicin (indolic), and one nucleoside (adenosine).The mechanisms for the lack of metabolite reduction differed between mutants and metabolites (Fig. 4B).med16 showed dysregulation of aliphatic GLSs and nucleosides at 24 h due to reduced levels in mock-treated controls.In contrast, med16 was deficient in Glucobrassicin reduction in response to infection.Mock-treated med18 exhibited elevated levels of both aliphatic and indolic GLSs, as we have reported 27 .This results in inadequate reduction of these metabolites in med18 which after infection displayed GLSs levels similar to those observed in mock-treated Col-0 (Fig. 4B).
Metabolites showing significant fold changes in each mutant, are shown (Supplementary Tables 6-7).For the full set of log 2 FCs of all metabolites in all mutants relative to Col-0, see Supplementary Table 2.
We used RT-qPCR to quantify mRNA levels encoding key enzymes in the aliphatic GLS pathway in mocktreated Col-0 and mutants (Fig. 4C).CYP79F1 and MAM1 mRNA levels were increased in med18 but decreased in med16, correlating with their aliphatic GLS levels (Fig. 4B).MYB28, MYB29 and MYB76 belong to the R2R3family of MYB TFs which regulate expression of CYP79F1 and MAM1 15 .In line with the effects observed for their target genes, MYB29 and MYB76 mRNA levels were decreased in mock-treated med16 but increased in med18 (Fig. 4D).For an overview of the GLS and phytoalexin metabolic pathways, see Fig. 4E.All significantly increased metabolites in Col-0 at 24 h relative to control, and their levels in mutants are shown in the heat map (Fig. 4A).Metabolites that showed a log 2 FC > 1 in Col-0, and their fold changes in each mutant are listed (Supplementary Table 7).Representative metabolites that were increased at 24 h in Col-0 but showed defect levels in one or several mutants are shown (Fig. 5A-E).
A set of amino acids were increased in Col-0 at 24 h (Supplementary Table 7).In contrast, tryptophan showed no significant induction in med16 and reduced induction in med18 (Fig. 5A).Lack of tryptophan-induction in med16 was mainly caused by elevated levels in mock-treated cells, while med18 was unable to induce tryptophan to the same extent as Col-0 in response to infection (Fig. 5A).Furthermore, the elevated leucine levels observed in Col-0 were missing in med16, med18 and cdk8 (Fig. 5B).
Four benzenoids were induced in Col-0 at 24 h (Supplementary Table 7).One was SA, which also showed induced levels in all mutants.However, we detected increased absolute SA-levels in mock-treated med18, med25 and cdk8 (Fig. 5C).SA is produced by hydroxylation at the second position of the benzene ring of benzoic acid and can also be named 2-OH-benzoic acid.Additional hydroxylation at different benzene ring positions creates different benzenoids such as 4-OH-bensoic acid and 2,4-di-OH-benzoic acid.Conjugation of the hydroxylated benzoic ring to a glucose moiety results in formation of the hydroxy-benzoic acid glucosides SAG (SA-glucoside) and SGE (SA-glucose ester) which causes translocation into different cellular compartments 28 .We identified two different glycosylated forms of SA (SA-glycoside_1 (SAG_1) and SA-glycoside_2 (SAG_2) in our LCMSassays.(Supplementary Fig. 3A-B).SAG_1 was present at low levels in Col-0 and did not increase upon infection, while SAG_2 was highly induced.Interestingly, SAG_2 levels were increased in med25.Even though Col-0 showed a higher fold-induction of SAG_2 (3.3-fold) compared to med25 (2.0-fold) at 24 h, the absolute levels were the same in mock-treated med25 as in infected Col-0 (Supplementary Fig. 3B).Similar elevated levels in mock-treated med25 were also found for three other metabolites that represent modified versions of benzoic acid: 4-hydroxybenzoic acid, 2,4-dihydroxybenzoic acid and Protocatechuic acid 3-glucoside (Supplementary www.nature.com/scientificreports/Fig. 3C-E), suggesting that elevated levels of several SA-related metabolites in med25 before infection might contribute to its resistance against P. syringae.ICS1 is the key enzyme for pathogen-induced SA production (Supplementary Fig. 4A).We found that the fold induction of ICS1 mRNA was significantly higher in med25 relative to the other Pst-infected lines at 24 h.(Supplementary Fig. 4B).Furthermore, expression levels of EDS1 mRNA, encoding one of the transcriptional activators of ICS1 expression, were increased in Pst-infected med25 relative to Col-0, med16 and med18 at 24 h, while it was reduced in cdk8 (Supplementary Fig. 4C).Interestingly, EDS1 physically interacts with CDK8 29 .In contrast, the other ICS1 transcriptional activator PAD4 did not show significant differences in any mutant.Responses downstream of SA are mediated by NPR1, a transcriptional co-activator for SA-dependent genes (NIMIN1 and PR1).We found that NIMIN1 and PR1 transcript levels were similar in med25 and Col-0 but impaired in med16, med18 and cdk8 (Supplementary Fig. 4D).
Camalexin and a set of related compounds were the most highly induced metabolites in Col-0.(Fig. 5E, Supplementary Table 7).However, camalexin induction was impaired in med25 and cdk8.Again, these differences were due to different mechanisms.In mock-treated med25, camalexin was elevated to even higher levels than Col-0 after infection.In contrast, mock-treated cdk8 displayed low levels but no increase at 24 h.This might contribute to their opposite phenotypes.The difference is likely due to defects in PAD3 expression, which encodes the key enzyme in the camalexin synthesis pathway (Supplementary Fig. 5A).PAD3 expression was higher in uninfected med25 but lower in cdk8 relative to Col-0 (Supplementary Fig. 5B).Finally, induction of some phytoprostanes and AMP was slightly defect in med16 and med18 (Supplementary Table 7).

Sugars, amino acids and jasmonates accumulate in med16 and cdk8 but are decreased in med25 at the late stage of infection
We made scatterplots representing changes in levels of all identified metabolites at 72 h relative to control.The distribution of log 2 -FCs was compared between each mutant and Col-0 using linear regression (Fig. 6A).To reveal deviations in mutants, we tested the null hypothesis that the slope of the linear regression was equal to one.Table 1 shows slope estimates and adjusted R^2 for the regression as well as p values for significance of deviation from slope equal one.med16 and cdk8 had significantly lower slopes indicating that their metabolic responses were more affected relative to Col-0, while the med25 slope indicates that it on average was less affected.The slope for med18 was similar to Col-0.These results correlate with the phenotypic differences between mutants.
For metabolites that were increased in mutants, we found a large overlap of 48 metabolites between med16 and cdk8 (Fig. 6B).Twenty-four of them were also increased in med18 (Supplementary Table 8).In line with its opposite phenotype only three were increased, while eight were reduced in med25.Using a less strict threshold we identified a set of amino acids (leucine, phenylalanine, 5-hydroxy-L-tryptophan, and tyrosine) which were increased in med16 and cdk8 but decreased in med25 (Supplementary Fig. 6A).The fold change for aspartic acid showed the reverse, being reduced in med16, and cdk8 but increased in med25.We also identified twentyfive lipids that where commonly increased in med16 and cdk8.One of them, the unsaturated lipid stigmasterol, was reduced in med25.Thus, stigmasterol levels correlate to bacterial growth in each mutant.Finally, twelve carbohydrates showed increased fold change in med16 and/or cdk8, and half of them were reduced in med25.In particular, trehalose showed the highest fold increase compared to Col-0 in cdk8 (log 2 FC = 3.7) but was decreased in med25 (log 2 FC = − 1.4).
The sensitive med16 and cdk8 mutants displayed increased jasmonate levels after infection (Fig. 6C).med16 showed accumulation of 12-hydroxylated-JA-Ile, cdk8 displayed pronounced increase of methylated JA (MEJA), while both mutants showed induced levels of the active form of JA (JA-Ile) (Supplementary Fig. 6B).α-linolenic acid (18:3), a fatty acid substrate of jasmonate biosynthesis, was also elevated in the med16 and cdk8 (Supplementary Fig. 6B), suggesting a broad and dysfunctional regulation of the jasmonate biosynthetic pathway in these mutants.The increased levels of jasmonates in med16 corroborated its increased mRNA levels of JA-related genes relative to the other mutants in mock-treated plants (Supplementary Fig. 6C).
Many JA-induced genes are regulated by MYC2.We found that mRNA levels of MYC2 in both med16 and med25 under control conditions were elevated relative to those detected in Col-0.However, induction of MYC2 was reduced in med16 and med25, resulting in similar levels of MYC2 in Pst-infected Col-0, med16, med25 and cdk8 at 24 h (Fig. 6D).MED25 is important for MYC2-dependent gene regulation 20 .Accordingly, induction of www.nature.com/scientificreports/JMT, a MYC2 target gene encoding an S-adenosyl-L-methionine:JA carboxyl methyltransferase that catalyzes formation of MEJA from JA, was severely reduced in med16 and med25 due to a combination of elevated levels before, and deficient induction after infection (Fig. 6D).cdk8 displayed elevated levels of JMT before infection but in contrast to med16 and med25, it showed the same fold-induction of JMT as Col-0.Thus, elevated MEJA levels could result from increased expression of JMT in cdk8.Two other MYC2 target genes, JAZ6 and LOX2, were uninduced in med25 after infection (Supplementary Fig. 6D).The same impairment was detected for med16 after infection whereas cdk8 showed accumulation of JAZ6.

Discussion
Mediator plays important roles for biotic and abiotic stress responses.Mutations in MED2, MED14, MED16, MED18, and MED25 causes impaired responses to drought, high salinity and cold 30,31 , and MED8, MED14, MED15, MED16, MED18, MED20, and MED25 are involved in responses to biotic stress 6,10,32 .Furthermore, Mediator subunits are important for the SA and JA/Ethylene signaling pathways, which control several aspects of defense signaling in plants 8,26,33 .Interactions between Mediator subunits and regulatory TFs are important for responses to biotrophic pathogens such as P. syringae 34,35 .Less is known regarding how Mediator mutations affect metabolite levels and how such changes influence infection susceptibility.We show that med16 and cdk8 are susceptible to P. syringae while med25 shows increased resistance compared to Col-0.We identify dysregulation of specific metabolites that can explain the phenotypic variations and show that differences in metabolite responses between Col-0 and mutants can result from pre-existing variations already in uninfected plants, or in how their levels change in response to infection.In uninfected cells, three metabolite categories showed differences in responses in mutants relative to Col-0 which might explain the variations in phenotypes for each of med16, med25 and cdk8.Phytoalexins, carbohydrates and GLSs were reduced in the susceptible med16 and cdk8 but were increased in the more resistant med25.This agrees with previous reports showing that the levels of these metabolites are induced in Col-0 in response to infections [36][37][38] .In particular, GLSs are known to increase in infected Col-0 and to play important roles in infection defense 39 .We found reduced levels of Sulforaphane and derivatives in uninfected, susceptible med16 and cdk8 but increased levels in the resistant med25.Sulforaphane is produced in response to several nonhost bacterial pathogens, and P. syringae strains adapted to Arabidopsis express SURVIVAL IN ARABIDOPSIS EXTRACTS (SAX) genes, which enable bacteria to detoxify host-produced sulforaphane 40 .We also identified two additional GLSs (Hirsutin and Neoglucobrassicin) that showed reduced levels in uninfected med16 and cdk8 and additional GLSs, carbohydrates and phytoalexins that were uniquely reduced in med16 or cdk8.In line with our metabolite profiling of uninfected Col-0 and mutants, our RNA-seq showed that med16 and cdk8 have reduced levels of transcripts encoding enzymes involved in GLS biosynthesis and metabolism and expression of PAD3 which encodes a key enzyme in synthesis of camalexin was increased in uninfected med25 but lower in cdk8.Finally, a set of metabolites were uniquely elevated in uninfected med25.The most common category was benzenoids, which are precursors for SA synthesis.We conclude that levels of GLSs and phytoalexins in mock-treated mutants correlate with their susceptibility to P. syringae infection.Since SA plays a major role in defense against P. syringae 41 , the elevated levels of several SA-related metabolites in med25 already before infection might contribute to its resistance.
In the early response to P. syringae, we identified a further set of metabolites whose fold-change deviate in mutants.One important category was amino acids, which play important functions as precursors for several defense-mediating secondary metabolites.Tryptophan is an important precursor for camalexin, indolic GLSs, and auxin (indole-3-acetic acid; IAA) synthesis.Our results show that tryptophan before infection are increased in med16 to the same levels as those we observe in Col-0 and the other mutants after infection.Phenylalanine, a precursor for flavonoids, anthocyanins, benzoic acids and SA, was induced in Col-0 and all mutants but its absolute levels were increased in med16 both before, and at the early timepoint after infection.A set of benzoic acids, among them SA, showed dysregulation in the mutants at the early timepoint after infection.We identified two differently regulated glycosylated forms of SA.SAG_1 was present at low levels in both Col-0 and med25, while SAG_2 was similarly induced in Col-0 and med25 but present in much higher levels in the mutant.These metabolites likely correspond to SA-glucoside (SAG) and SA-glucose ester (SGE), but were indistinguishable using LCMS.Based on previously reported results showing that the levels of SGE in Arabidopsis is lower than SAG and that SGE is much less induced in response to wounding compared to SAG 42 , it is likely that SGE in our assays corresponds to SAG_1 and SAG to SAG_2.Camalexin, and a set of modified Camalexin-related metabolites were uninduced in both med25 and cdk8 relative to Col-0.Again, the mechanisms for the lack of induction differed between mutants.Uninfected med25 displayed Camalexin levels even higher than infected Col-0 and they did not increase further upon infection.In contrast, cdk8 showed low Camalexin levels both before and after infection.Finally, oxylipins with conjugated OPDA were induced in all lines, but most prominently in Col-0, med16 and cdk8 at the early time point after infection.
At the late time point after infection, we observed several differences between Col-0 and mutants.Generally, med16 and cdk8 showed larger effects, while med25 showed less.Nearly fifty metabolites were present at higher levels at the late time point in both med16 and cdk8 relative to Col-0 but were unaffected or present at lower levels in med25.Of these, a set of amino acids and twenty-five lipids were commonly increased in med16, and cdk8.One of them was stigmasterol, which was also reduced in med25.Stigmasterol is synthesized at pathogen inoculation sites where it integrates into plant cell membranes and favors susceptibility to bacterial pathogens 43 .Also, a set of carbohydrates showed increased levels in infected med16 and cdk8, while they were decreased in med25.In particular, trehalose was highly increased in cdk8 but severely decreased in med25.Application of exogenous trehalose was recently shown to significantly increase susceptibility to P. syringae 44 , suggesting that its elevated levels might contribute to the increased and decreased bacterial content in cdk8 and med25, respectively.Finally, we identified increased levels of jasmonates in med16 and cdk8 at the late time point.Jasmonates comprise a family of oxylipins including JA that regulate various aspects of plant immunity and development.It is therefore likely that the dysregulation of JA we observe contributes to their phenotypic behavior in response to infection.One possible explanation to the increased levels of some metabolites could originate from the infecting bacteria, especially those that are common between bacteria and plants like amino acids, lipids and carbohydrates.However, our infection results show that cdk8 at 72 h after infection contains twice as many bacteria compared to med16 but we could not detect any clear difference for these metabolite categories between the two mutants (cf.Fig. 1B and Supplementary Fig. 6A).www.nature.com/scientificreports/Using a combination of metabolomics and RNA-seq and four Mediator mutants to study molecular mechanisms for how Arabidopsis respond to P. syringae infection, we identify a set of unique metabolites and metabolite categories that show different types of defects in mutants.These differences correlate with defects in regulation of genes encoding key enzymes or regulatory TFs that are important for a proper infection response.Our results form a base for future research that will hopefully result in plants and crops which are more resistant to infections.

Bacterial infection and colony forming unit (CFU) assay
Single colonies were picked and grown in Kings Broth (KB) liquid media containing 50 µg/ml rifampicin at 28 °C over night.Cultures were diluted to OD 600 of 0.3 and grown to an OD 600 of 0.8-1.Exponentially growing bacteria was collected, washed twice in 10 mM MgCl 2 and resuspended in the same solution at a concentration of 2 × 10 6 CFU/ml.4-6 lower leaves of 5-6-week-old plants were injected on their abaxial side with either Pst.DC3000 suspension (infections) or 10 mM MgCl 2 (controls).Leaves from 2-3 plants were pooled for each biological replicate.For quantification of bacterial growth, 40 mg pooled leaf samples were grinded in 1 mL 10 mM MgCl 2 , serially diluted and plated on KB plates containing 50 µg/ml rifampicin.Bacterial numbers were determined using the following formula: CFU = ((Number of colonies × volume × dilution factor)/volume plated)/sample weight.

GC-MS profiling
Derivatization and GC-MS analysis were performed as described 47 .0.5 μL of the derivatized sample was injected in splitless mode by an L-PAL3 autosampler (CTC Analytics AG, Switzerland) into an Agilent 7890B gas chromatograph equipped with a 10 m × 0.18 mm fused silica capillary column with a chemically bonded 0.18 μm Rxi-5 Sil MS stationary phase (Restek Corporation, U.S.) The injector temperature was 270 °C, the purge flow rate was 20 mL/min, and the purge was turned on after 60 s.The gas flow rate through the column was 1 mL/ min.The column temperature was held at 70 °C for 2 min, then increased by 40 °C/min to 320 °C and held there for 2 min.The column effluent was introduced into the ion source of a Pegasus BT time-of-flight (TOF) mass spectrometer, GC/TOFMS (Leco Corp., St Joseph, MI, USA).The transfer line and the ion source temperatures were 250 °C and 200 °C, respectively.Ions were generated by a 70-eV electron beam at an ionization current of 2.0 mA, and 30 spectra/s were recorded in the mass range m/z 50-800.The acceleration voltage was turned on after a solvent delay of 150 s.The detector voltage was 1800-2300 V.

LC-MS profiling
The samples were reconstituted in 20 µL of 50% (v/v) methanol before analysis.Each batch of samples were initially analyzed in positive mode, followed by a switch to negative mode for a second injection of each sample after analyzing all samples within the batch.The chromatographic separation was performed on an Agilent 1290 Infinity UHPLC-system (Agilent Technologies, Waldbronn, Germany).Two μL of each sample were injected onto an Acquity UPLC HSS T3, 2.1 × 50 mm, 1.8 μm C18 column in combination with a 2.1 mm × 5 mm, 1.8 μm VanGuard precolumn (Waters Corporation, Milford, MA, USA) held at 40 °C.The gradient elution buffers were A (0.1% formic acid) and B (75/25 (vol/vol) acetonitrile:2-propanol, 0.1% formic acid), and the flowrate was 0.5 mL/min.The compounds were eluted with a linear gradient consisting of 0.1-10% B over 2 min.B was then increased to 99% over 5 min and held at 99% for 2 min.B was then decreased to 0.1% over 0.3 min and the flowrate was increased to 0.8 mL/min for 30 s.These conditions were kept for 0.9 min, after which the flow-rate was reduced to 0.5 mL min −1 for 0.1 min before the next injection.The compounds were detected using an Agilent 6546 Q-TOF mass spectrometer equipped with a jet stream electrospray ion source operating in positive or

Figure 1 .
Figure 1.Susceptibility of Col-0 and Mediator mutants to infection by P. syringae.Four leaves each of 5-weekold plants were injected with either 10 mM MgCl 2 (control) or 1 × 10 6 CFU/ml of the virulent Pst.DC3000.(A) Bacteria were extracted from 100 mg of grounded tissue from mock-treated and infected plants and colonies formed from 10 × serial dilutions (D1 to D6) at the 72-h time point are shown.(B) The box plot represents the means of CFUs of the D3 dilution from four independent infections.Asterisks indicate significant differences between each mutant and Col-0 (Student's t test, *p < 0.05; **p < 0.01; ***p < 0.001) and the error bars show mean standard deviation (SD) of four independently infected plants.(C) An illustrative picture of disease symptoms visible as necrotic and chlorotic lesions on leaf surfaces from three independently infected plants (infection 1-3) and one control experiment.

Figure 2 .
Figure 2. Non-targeted metabolomic profiling of Col-0 and Mediator mutants at the 24-and 72-h time points after infection.(A) Multivariate statistical analysis of GC-MS and LC-MS non-targeted metabolite profiles of infected and mock-treated Col-0, med16, med18, med25 and cdk8 leaves at the 24-and 72-h time points are shown as a PCA score plot.Each data point represents the entire metabolome (GC-MS plus LC-MS) for each replicate and are arranged in the first and second dimensions (x and y respectively).(B) Number of metabolites with statistically significant (Student's t test, p < 0.05) altered levels in mutants compared to Col-0 at the indicated time points and treatments.

Figure 3 .
Figure 3.Comparison of metabolites and mRNAs with altered levels in Mediator mutants relative to Col-0 in mock-treated plants.(A) Upset diagram illustrating both the overlap and uniqueness of metabolites that display altered levels in mock-treated mutants relative to Col-0.Only metabolites that show statistically significant differences (log 2 -FC > 0.5 or < − 0.5 and p < 0.05) at both the 24-and 72-h time points are included.The total number of metabolites that display altered levels in each mutant are represented as red (increased) and blue (decreased) bars to the left.(B) Metabolites that displayed statistically distinct levels in mock-treated mutants relative to mock-treated Col-0 were grouped into nineteen metabolite categories (see Supplementary Table3).Sizes of circles represent the ratio of the number of metabolites in each category that display significantly different levels in mutants relative to Col-0 and the total amount of metabolites detected for each category in our full data set of 222 metabolites.Numbers to the right of each circle represent the total number of metabolites that show significantly changed levels in each mutant.(C) GO enrichment analysis of alterations in defined KEGG-pathways[50][51][52] of differentially expressed genes (DEGs) in each mutant relative to Col-0.The sizes of circles represent the significance (-log 10 of Benjamini-Hochberg adjusted p-value) of the enrichment for each GO and numbers to the right of circles represent the number of DEGs for each mutant in the respective GO.

3
Figure 3.Comparison of metabolites and mRNAs with altered levels in Mediator mutants relative to Col-0 in mock-treated plants.(A) Upset diagram illustrating both the overlap and uniqueness of metabolites that display altered levels in mock-treated mutants relative to Col-0.Only metabolites that show statistically significant differences (log 2 -FC > 0.5 or < − 0.5 and p < 0.05) at both the 24-and 72-h time points are included.The total number of metabolites that display altered levels in each mutant are represented as red (increased) and blue (decreased) bars to the left.(B) Metabolites that displayed statistically distinct levels in mock-treated mutants relative to mock-treated Col-0 were grouped into nineteen metabolite categories (see Supplementary Table3).Sizes of circles represent the ratio of the number of metabolites in each category that display significantly different levels in mutants relative to Col-0 and the total amount of metabolites detected for each category in our full data set of 222 metabolites.Numbers to the right of each circle represent the total number of metabolites that show significantly changed levels in each mutant.(C) GO enrichment analysis of alterations in defined KEGG-pathways[50][51][52] of differentially expressed genes (DEGs) in each mutant relative to Col-0.The sizes of circles represent the significance (-log 10 of Benjamini-Hochberg adjusted p-value) of the enrichment for each GO and numbers to the right of circles represent the number of DEGs for each mutant in the respective GO.
Figure 3.Comparison of metabolites and mRNAs with altered levels in Mediator mutants relative to Col-0 in mock-treated plants.(A) Upset diagram illustrating both the overlap and uniqueness of metabolites that display altered levels in mock-treated mutants relative to Col-0.Only metabolites that show statistically significant differences (log 2 -FC > 0.5 or < − 0.5 and p < 0.05) at both the 24-and 72-h time points are included.The total number of metabolites that display altered levels in each mutant are represented as red (increased) and blue (decreased) bars to the left.(B) Metabolites that displayed statistically distinct levels in mock-treated mutants relative to mock-treated Col-0 were grouped into nineteen metabolite categories (see Supplementary Table3).Sizes of circles represent the ratio of the number of metabolites in each category that display significantly different levels in mutants relative to Col-0 and the total amount of metabolites detected for each category in our full data set of 222 metabolites.Numbers to the right of each circle represent the total number of metabolites that show significantly changed levels in each mutant.(C) GO enrichment analysis of alterations in defined KEGG-pathways[50][51][52] of differentially expressed genes (DEGs) in each mutant relative to Col-0.The sizes of circles represent the significance (-log 10 of Benjamini-Hochberg adjusted p-value) of the enrichment for each GO and numbers to the right of circles represent the number of DEGs for each mutant in the respective GO.

Figure 4 .
Figure 4. Impaired reduction of specific metabolite levels is caused by different mechanisms in med16 and med18.(A) Heat map and hierarchical clustering of metabolites that show a log 2 -FC > 0.5 or < − 0.5, and p < 0.05 in infected relative to mock-treated Col-0 at the 24-h time point.(B) Bar graphs of normalized mean peak areas from four independently infected or mock-treated Col-0 and mutant plants at the 24-h time point.(C) Quantification of transcript levels for two of the aliphatic-GSL biosynthesis enzymes (CYP79F1 and MAM1) in uninfected Col-0 and mutants using RT-qPCR.(D) Quantification of transcript levels for two of the aliphatic-GSL biosynthesis regulating transcription factors (MYB29 and MYB76) in uninfected Col-0 (black bars) and mutants (light green bars) using RT-qPCR.Asterisks in B-D indicate pairwise significant differences (Student's t test, *p < 0.05; **p < 0.01; ***p < 0.001) for comparisons indicated by the brackets.Error bars represent the SD of four independent infection replicates for metabolite levels and three replicates for transcript levels.(E) Simplified overview of the aliphatic-GLS, indolic-GLS and phytoalexin synthetic pathways.Each arrow indicates multiple enzymatic steps and only the enzymes analyzed here are indicated.

Figure 5 .
Figure 5.All mediator mutants have unique alterations in the induction of specific metabolites compared to Col-0 at the early time point after infection.Metabolite levels of L-Tryptophan (A), L-Leucine (B), Salicylic acid_1 (C), MGMG (18:4;O/0:0) (D), and camalexin (E) in infected and mock-treated Col-0 and mutants at the 24-h time point.Asterisks indicate pairwise significant differences between Col-0 and each mutant, or between mock-treated and infected lines as indicated by the brackets (Student's t test, *p < 0.05; **p < 0.01; ***p < 0.001) and the error bars show the SD of four independently infected plants.

Figure 6 .
Figure 6.med25 shows an attenuated infection response whereas med16 and cdk8 show the highest alterations in metabolite levels compared to Col-0 at the late stage of infection.(A) Scatter plots of the relationship between metabolite changes at 72 h p.i. (relative to control at 72 h) between each mutant (x-axis) and Col-0 (y-axis).(B) Upset diagram illustrating the overlap and uniqueness in increased and decreased metabolite levels in each mutant relative to Col-0 at 72 h p.i.Total number of metabolites displaying changed levels in each mutant are represented as red (increased) and blue (decreased) bars to the left.(C) Distribution of metabolites displaying changed levels in each mutant for each of the nineteen metabolite categories.Sizes of circles represent the ratio of the of the number of metabolites in each category that display significantly distinct levels in mutants relative to Col-0 and the total amount of metabolites detected for each category in our full data set of 222 metabolites.Numbers to the right of each circle represent the total number of metabolites that show significantly changed levels in each mutant.In (A) a significance cutoff of log 2 -FC > 0.5 or < − 0.5 and p < 0.05) was used.In (B, C) a significance cutoff of log 2 -FC > 1 or < − 1 and p < 0.05) was used.The experiments were performed using four independently infected plants.(D) Levels of two JA activated genes (MYC2 and JMT) in mock-treated and infected cells at the 24-h time point.Asterisks indicate pairwise significant differences between mock-treated and infected lines as indicated by the brackets (Student's t test, *p < 0.05; **p < 0.01; ***p < 0.001) and the error bars show the SD of three independently infected plants. https://doi.org/10.1038/s41598-024-57192-x

Table 1 .
Summary of linear regression estimates of log 2 -fold changes for mutants relative to Col-0.